• Journal of Digital Convergence
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• v.12 no.11
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• pp.99-112
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• 2014

This study the impact on the real earnings management credit rating (RANK), and looked at the impact on the real earnings management grade credit rating changes (decrease, increase) the effects in detail. firm for a total of 06 years for firm that are listed on the Korea Stock Exchange from 2008 to 2013 for the hypothesis - using the proceeds of the year 2,583 sample were analyzed to study. A regression analysis of the relevance of the credit rating (RANK) and real earnings measured results between the credit rating and a measure of real earnings management ACFO and ADE (+) between AMC (-) IFRS and receive relevant ADE between(+) between AMC (-) if the credit rating (RANK) is increased ACFO and is significantly sound level at 1% showed the relevance of (+) did not significantly ADE (+) 10% of AMC if the credit rating fell ACFO is (-) from AMC show the relevance of positive credit rating is dropped capital letter showed for performing real earnings management of positive even give up the future cash flow in order to reduce the cost.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.227-235
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• 2005

Document filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. In this paper, we treat document filtering problem as binary document classification problem and we proposed the News Filtering system based on the Bayesian Classifier. For we perform filtering, we make an experiment to find out how many training documents, and how accurate relevance checks are needed.

• Tunnel and Underground Space
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• v.13 no.2
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• pp.108-116
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• 2003

Measured convergence data of a tunnel were investigated by means of statistical and regression analysis, where the rock mass were mainly composed of andesite and granite. The rock mass around tunnel were classified by RMR method into five different ratings, and then convergence data which belong to individual ratings were statistically processed to find out the appropriate regression equations. Exponential equations were better coincided with measured data than logarithmic equations. As the number of rock mass rating was increased, the magnitude and standard deviation of convergence were increased. Final convergence data were also investigated to study the relevance with both maximum displacement rate and early measured convergence. Some brief results of their relevance are presented. For instance, the regression coefficient between final convergence and maximum displacement rate was turned out to be 0.87 for this studied tunnel.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.6
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• pp.338-345
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• 2013

The purpose of this study is to analyze the correlation between the 'Fenestration Energy Consumption Efficiency Rating System' (hereafter referred to as FECERS) and building energy consumption. 'EnergyPlus' was used for the calculation of energy consumption in apartments and office buildings, according to FECERS's rating and SHGC. The result indicates that the FECERS has high correlation with apartments, but has low correlation with office buildings. Also, it indicates that office buildings have a large impact from SHGC, which is not reflected in the FECERS. Consequently, the FECERS needs to be improved, by adding optical properties to assessment items. Additional study is required to establish the fenestration rating system, which, on the basis of this work, has high relevance to building energy consumption.

• Journal of the Korean Society for information Management
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• v.17 no.1
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• pp.103-127
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• 2000

This study compared finding economic and energy information on the WWW to finding the same information on DIALOG, a traditional search service. Professional searchers answered 20 questions for end users using either of DIALOG and one Internet database (general search engine or Web database). The relevance of the results in both sets of answers was ranked by searchers and end-users, respectively. The study found that searching for information on the Web took at least twice as long as it did when using DIALOG. Relevance rating was a little higher for materials found on DIALOG. However, the relevance rating difference between two systems was not so higher than we expected. From the research results, we conclude that Internet database including Web database and general search engines is providing valuable information of economic and energy subject areas.

• Korean Journal of Schizophrenia Research
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• v.17 no.1
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• pp.36-42
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• 2014

Objectives : The dysfunctional neural networks underlying self-evaluation in schizophrenia are overlapped with the neural structures involved in emotion regulation. The purpose of this study was to investigate the influence of anhedonia on the self-evaluation attitude of patients with schizophrenia. Methods : Twenty healthy controls and twenty patients with schizophrenia performed a self-evaluation task, presenting a pair of the face (self, familiar other, and unfamiliar other) and word (negative, neutral, and positive noun) at the same time. Participants were asked to evaluate relevance between the pairs by pressing a corresponding button. Relevance rating scores were compared between the groups and were correlated with the severity of physical and social anhedonia. Results : Patients evaluated the condition of a self face with a negative word and a familiar face with a negative word to be more relevant than healthy controls. In the patient group, the scores of relevance rating in the condition of an unfamiliar other face with a negative word were positively correlated with the anhedonia scale scores (physical : r=0.486, p=0.030 ; social : r=0.499, p=0.025). There was no correlation between the self-evaluation attitude and the severity of anhedonia. Conclusion : Patients with schizophrenia evaluate themselves badly in only negative circumstances, and anhedonia is not related to self-evaluation, but rather other-evaluation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.221-226
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• 2021

Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.

• Korean Journal of Construction Engineering and Management
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• v.24 no.4
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• pp.35-43
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• 2023

This study is aiming to analyze the relevance of rating criteria and these criteria may have effects on policy purpose of 'Construction Skilled Workers' Rank System', that are inflow of new comer, improvement of job performance, and decrease job-change. Closely related systems, electronic card system and prevailing wage system, are considered by assuming three scenarios, although they are not introduced yet. Empirical survey shows that the relevance is above average. The empirical result of regression also predicts that policy target might be mostly satisfied. Policy purpose is regressed on rating criteria, they are career, qualification, education and training, award. Career and award have positive impact on inflow of new comer. All four criteria have significant impacts on improvement of job performance. Award has strong effects on decreasing job-change. Especially, when electronic card system and prevailing wage system are adopted simultaneously with 'Construction Skilled Workers' Rank System', the level of satisfaction of policy purpose would be higher.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1062-1072
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• 2004

Collaborative filtering systems based on ｛user-document｝ matrix are effective in recommending web documents to user. But they have a shortcoming of decreasing the accuracy of recommendations by the first rater problem and the sparsity. This paper proposes the automatic preference rating method that generates user profile to solve the shortcoming. The profile in this paper is content-based collaborative user profile. The content-based collaborative user profile is generated by combining a content-based user profile with a collaborative user profile by mutual information method. Collaborative user profile is based on ｛user-document｝ matrix in collaborative filtering system, thus, content-based user profile is generated by relevance feedback in content-based filtering systems. After normalizing combined content-based collaborative user profiles, it automatically rates user preference by reflecting normalized profile in ｛user-document｝matrix of collaborative filtering systems. We evaluated our method on a large database of user ratings for web document and it was certified that was more efficient than existent methods.